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Colorized Image Forgery Detection based on Similarity Measurement of Gaussian Mixture Distribution

Volume 14, Number 3, March 2018, pp. 445-452
DOI: 10.23940/ijpe.18.03.p5.445452

Ze Yanga, Jianhou Ganb, Juxiang Zhoub, Bin Wena, and Jun Wangb

aCollege of Computer Science and Technology, Yunnan Normal University, Kunming, 650500, China
bKey Laboratory of Education Informalization for Nationalities of Ministry of Education, Yunnan Normal University, Kunming, 650500, China

(Submitted on December 8, 2017; Revised on January 16, 2018; Accepted on February 17, 2018)


Abstract:

In the era of rapid development of multi-media information, forgery detection has become an important research field of digital image security. This paper proposes a new method to detect the forged image generated by deep learning. First, the feature matrix is constructed through extracting each pixel value of channels a and b in Lab color space for the real and the forged image training set, respectively, which is used to fit the Gaussian Mixture Model (GMM) distribution. Then, the Expectation Maximization (EM) adaptation algorithm is used to re-fit the GMM for test image using the obtained GMM parameter as prior information. Finally, the similarity between two GMM is calculated for forgery detection. Experiments show that the proposed method is more accurate than the traditional SVM for forgery detection.

 

References: 13

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  8. V. Thirunavukkarasu, J. S. Kumar, “Passive Image Tamper Detection Based on Fast Retina Key Point Descriptor” in IEEE International Conference on Advances in Computer Applications. IEEE, 2017:279-285.
  9. N. Vasconcelos, P. Ho, P. Moreno, “The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition” in Computer Vision - ECCV 2004. DBLP, 2004:430-441.
  10. Y. E. Xi, “Blind Image Tamper Detection Algorithm Based on Radon and Fourier-Mellin Transform” in Signal Processing, 2010:212-215.
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  12. J. Zhong, Y. Gan, J. Young, et al, “Copy Move Forgery Image Detection via Discrete Radon and Polar Complex Exponential Transform-Based Moment Invariant Features”, in International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(02): 1754005.
  13. R. Zhang, P. Isola, A. A. Efros, “Colourful Image Colorization” in European Conference on Computer Vision. Springer, Cham, 2016:649-666.

 

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